code: def kBestSuggestions(
      self, x, k=1, exclude_double_configurations=True
  ):
      assert type(x) == pd.Series
      if k < -1 or k == 0:
          raise ValueError(
              "Number of neighbors k cannot be zero or negative."
          )
      nearest_datasets, distances = self.kNearestDatasets(
          x, -1, return_distance=True
      )
  
      kbest = []
  
      added_configurations = set()
      for dataset_name, distance in zip(nearest_datasets, distances):
          best_configuration = self.best_configuration_per_dataset[
              dataset_name
          ]
  
          if best_configuration is None:
              self.logger.info(
                  "Found no best configuration for instance %s"
                  % dataset_name
              )
              continue
  
          if exclude_double_configurations:
              if best_configuration not in added_configurations:
                  added_configurations.add(best_configuration)
                  kbest.append(
                      (dataset_name, distance, best_configuration)
                  )
          else:
              kbest.append((dataset_name, distance, best_configuration))
  
          if k != -1 and len(kbest) >= k:
              break
  
      if k == -1:
          k = len(kbest)
      return kbest[:k]
  
file_path: auto-sklearn/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py
imports: import numpy
  import pandas
  import sklearn.utils
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 4 for calling the KNearestDatasets obj's underlying member method code with example usage: obj.kBestSuggestions(x, k, exclude_double_configurations) and returns: kbest